Download e-book for iPad: Cross Disciplinary Biometric Systems by Chengjun Liu, Vijay Kumar Mago

By Chengjun Liu, Vijay Kumar Mago

Cross disciplinary biometric platforms support develop the functionality of the normal structures. not just is the popularity accuracy considerably enhanced, but additionally the robustness of the platforms is vastly greater within the demanding environments, akin to various illumination stipulations. via leveraging the pass disciplinary applied sciences, face popularity structures, fingerprint reputation platforms, iris reputation structures, in addition to snapshot seek structures all gain when it comes to reputation functionality. Take face reputation for an instance, which isn't in simple terms the main typical means people realize the identification of one another, but additionally the least privacy-intrusive ability simply because humans exhibit their face publicly each day. Face popularity structures reveal wonderful functionality once they capitalize at the leading edge principles throughout colour technology, arithmetic, and computing device technology (e.g., trend attractiveness, laptop studying, and photograph processing). the radical principles result in the advance of latest colour types and powerful colour gains in colour technology; leading edge gains from wavelets and information, and new kernel tools and novel kernel types in arithmetic; new discriminant research frameworks, novel similarity measures, and new photo research tools, corresponding to fusing a number of photograph positive aspects from frequency area, spatial area, and colour area in machine technology; in addition to method layout, new ideas for procedure integration, and varied fusion ideas, akin to the characteristic point fusion, determination point fusion, and new fusion techniques with novel similarity measures.

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The R component image in the RGB color space and the V component image in the HSV color space, for instance, have been shown more effective for face recognition than the component images in several other color spaces [29]. As Gabor wavelets model quite well the receptive field profiles of cortical simple cells [3], [2], researchers have been applying Gabor wavelets to convolve images for extracting efficient features. Since the Gabor convolved image representation is able to capture salient visual properties such as spatial localization, orientation 38 Z.

Our previous research on comparative assessment of 12 different color spaces reveals that the R component image in the RGB color space is more effective for face recognition than the component images in the other color spaces [29]. We therefore choose the R component image to derive the discriminative color facial parts as shown in Fig. 2. Next, the Gabor filtered images corresponding to each facial part are grouped together based on adjacent scales and orientations to form Multiple Scale and Multiple Orientation Gabor Image Representation (MSMO-GIR).

IEEE Transactions on Pattern Analysis and Machine Intelligence 23(12), 1338– 1350 (2001) 7. : Digital Image Processing, 3rd edn. Pearson Prentice Hall (2008) 8. : Global color constancy: Recognition of objects by use of illumination invariant properties of color distributions. Journal of the Optical Society of America A 11(11), 3003–3010 (1994) 9. : Face detection: A survey. Computer Vision and Image Understanding 83, 236–274 (2001) 10. : Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002) 11.

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